(Created 2011-09-01.)

COMPUTER VISION | FMA270 |

**Aim**

The aim of the course is to give an overview of the theory and practically useful methods in computer vision, with applications within e.g. vision systems, non-invasive measurements and augmented reality. In addition the aim is to make the student develop his or her ability in problem solving, with and without a computer, using mathematical tools taken from many areas of the mathematical sciences, in particular geometry, optimization, mathematical statistics, invariant theory and transform theory.

*Knowledge and understanding*

For a passing grade the student must

be able to describe and give an informal explanation of the mathematical theory behind some central algorithms in computer vision (the least squares method, Newton based optimization and stochastic methods).

*Skills and abilities*

For a passing grade the student must

be able to show good ability to independently identify problems which can be solved with methods from computer vision, and be able to choose an appropriate method.

be able to independently apply basic methods in computer vision to problems which are relevant in industrial applications or research.

with proper terminology, in a well-structured way and with clear logic, be able to explain the solution to a problem in computer vision.

**Contents**

Projective geometry. Geometric transformations. Modelling cameras. Stereo vision. Photogrammetry. Recognition. 3D-modeling. Geometry of surfaces and their silhouettes. Visualisation.

**Literature**

R. Szeliski, Computer Vision: Algorithms and Applications, Springer Verlag 2010. ISBN: 9781848829343